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Adapting reservoir operation to a changing climate is important to improve water system performance toward benefits including water security, and energy production. However, managers still need to know if and how reoperation can also assist long-term mitigation of climate change impacts on aquatic ecosystems, creating, for example, opportunities to revert fish migration and recruitment losses. This paper investigates the operational adaptive capacity of water systems to mitigate climate change impacts on both hydropower and aquatic ecosystems, with a detailed representation of fish species recruitment response to flow regime changes. The methodology framework combines hydroclimatic modeling, explicit stochastic reservoir operation modeling, and predictive modeling of migratory fish recruitment abundance, illustrated using the large-scale hydropower system of the Paraná River Basin in Brazil. Results identified that operating policies can be adapted to improve hydropower production under a changing climate with drier conditions by 2% to 8% compared with current operating policies. Although insufficient to eliminate all energy losses that climate change may cause, the optimized operation provided flexibility to adjust flow releases and reduce the likelihood of future severe multiyear deficits, which are very harmful to fish populations. Adapting operation to climate change sacrificed fish recruitment performance over a few years of the time horizon to maintain an overall higher storage, but it also improved the chances of producing flow releases in magnitude, timing, and duration during long drier periods that prevented more severe impacts of climate change on fish recruitment and population. This indicates that it might be possible to have synergies in adapting reservoir operation to not only prevent energy losses, but also to improve fish recruitment under climate change. The ecosystem resiliency under adapted operation increased up to 2 times compared with the isolated climate change effect. [ABSTRACT FROM AUTHOR]